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buriburisuri / sugartensor

A slim tensorflow wrapper that provides syntactic sugar for tensor variables. This library will be helpful for practical deep learning researchers not beginners.
MIT License
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Does it not work while using the newest tensorflow? #10

Closed LCLL closed 7 years ago

LCLL commented 7 years ago

It seems that it doesn't work when using the newest tensorflow.

As to the MNIST sample:

Traceback (most recent call last): File "test.py", line 18, in loss = logit.sg_ce(target=y) File "/usr/local/lib/python2.7/dist-packages/sugartensor/sg_main.py", line 151, in wrapper out = func(tensor, tf.sg_opt(kwargs)) File "/usr/local/lib/python2.7/dist-packages/sugartensor/sg_loss.py", line 44, in sg_ce out = tf.identity(tf.nn.sparse_softmax_cross_entropy_with_logits(tensor, opt.target), 'ce') File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_ops.py", line 1684, in sparse_softmax_cross_entropy_with_logits labels, logits) File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_ops.py", line 1533, in _ensure_xent_args "named arguments (labels=..., logits=..., ...)" % name) ValueError: Only call sparse_softmax_cross_entropy_with_logits with named arguments (labels=..., logits=..., ...)

buriburisuri commented 7 years ago

@LCLL Thanks for your information. I'll check this on newest tensorflow.